OpenRaiser/Intern-Atlas huggingface dataset:
Adaptively Customizing Activation Functions for Various Layers
Venue: arXiv cs.CV 2021
Year: 2021

To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and ReLU. To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC and COCO) . To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta and ADAM) and different recognition tasks like classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision and generalization, and it can surpass other popular methods like ReLU and adaptive functions like Swish in almost all experiments in terms of overall performance.The code is publicly available at https://github.com/HuHaigen/Adaptively-Customizing-Activation-Functions. The package includes the proposed three adaptive activation functions for reproducibility purposes.
A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder
Venue: arXiv cs.CL 2021
Year: 2021

Bipolar disorder is a mental health disorder that causes mood swings that range from depression to mania. Diagnosis of bipolar disorder is usually done based on patient interviews, and reports obtained from the caregivers of the patients. Subsequently, the diagnosis depends on the experience of the expert, and it is possible to have confusions of the disorder with other mental disorders. Automated processes in the diagnosis of bipolar disorder can help providing quantitative indicators, and allow easier observations of the patients for longer periods. Furthermore, the need for remote treatment and diagnosis became especially important during the COVID-19 pandemic. In this thesis, we create a multimodal decision system based on recordings of the patient in acoustic, linguistic, and visual modalities. The system is trained on the Bipolar Disorder corpus. Comprehensive analysis of unimodal and multimodal systems, as well as various fusion techniques are performed. Besides processing entire patient sessions using unimodal features, a task-level investigation of the clips is studied. Using acoustic, linguistic, and visual features in a multimodal fusion system, we achieved a 64.8% unweighted average recall score, which improves the state-of-the-art performance achieved on this dataset.
Complex Functional Maps : a Conformal Link Between Tangent Bundles
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces. A key property of these maps is their orientation awareness. More specifically, we demonstrate that unlike regular functional maps that link functional spaces of two manifolds, our complex functional maps establish a link between oriented tangent bundles, thus permitting robust and efficient transfer of tangent vector fields. By first endowing and then exploiting the tangent bundle of each shape with a complex structure, the resulting operations become naturally orientationaware, thus favoring orientation and angle preserving correspondence across shapes, without relying on descriptors or extra regularization. Finally, and perhaps more importantly, we demonstrate how these objects enable several practical applications within the functional map framework. We show that functional maps and their complex counterparts can be estimated jointly to promote orientation preservation, regularizing pipelines that previously suffered from orientation-reversing symmetry errors.
The Values and Limits of Altmetrics
Venue: arXiv cs.DL 2021
Year: 2021

Altmetrics are tools for measuring the impact of research beyond scientific communities. In general, they measure online mentions of scholarly outputs, such as on online social networks, blogs, and news sites. Some stakeholders in higher education have championed altmetrics as a new way to understand research impact and as an alternative or supplement to bibliometrics. Contrastingly, others have criticized altmetrics for being ill conceived and limited in their use. This chapter explores the values and limits of altmetrics, including their role in evaluating, promoting, and disseminating research.
Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring
Venue: arXiv cs.LG 2021
Year: 2021

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach based on graph convolutional networks and recurrent neural networks that uses information directly from the process model. An experimental evaluation on real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.
Data Privacy and Utility Trade-Off Based on Mutual Information Neural Estimator
Venue: arXiv cs.IT 2021
Year: 2021

In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. Nevertheless, it is challenging to characterize the mutual information accurately with small sample size or unknown distribution functions. In this article, we propose a privacy funnel based on mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Instead of computing mutual information in traditional way, we estimate it using an MINE, which obtains the estimated mutual information in a trained way, ensuring that the estimation results are as precise as possible. We employ estimated mutual information as a measure of privacy and utility, and then form a problem to optimize data utility by training a neural network while the estimator's privacy discourse is less than a threshold. The simulation results also demonstrated that the estimated mutual information from MINE works very well to approximate the mutual information even with a limited number of samples to quantify privacy leakage and data utility retention, as well as optimize the privacy utility trade-off.
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network
Venue: arXiv cs.CV 2021
Year: 2021

The paper focuses on improving the recent plug-and-play patch rescaling module (PRM) based approaches for crowd counting. In order to make full use of the PRM potential and obtain more reliable and accurate results for challenging images with crowd-variation, large perspective, extreme occlusions, and cluttered background regions, we propose a new PRM based multi-resolution and multi-task crowd counting network by exploiting the PRM module with more effectiveness and potency. The proposed model consists of three deep-layered branches with each branch generating feature maps of different resolutions. These branches perform a feature-level fusion across each other to build the vital collective knowledge to be used for the final crowd estimate. Additionally, early-stage feature maps undergo visual attention to strengthen the later-stage channels understanding of the foreground regions. The integration of these deep branches with the PRM module and the early-attended blocks proves to be more effective than the original PRM based schemes through extensive numerical and visual evaluations on four benchmark datasets. The proposed approach yields a significant improvement by a margin of 12.6% in terms of the RMSE evaluation criterion. It also outperforms state-of-the-art methods in cross-dataset evaluations.
On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne
Venue: arXiv cs.NE 2021
Year: 2021

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered much harder than Chess and that until very recently was considered infeasible for Artificial Intelligence methods. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees, commonly referred as UCT. The UCT attempts to nicely balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT mathematical expression. We compare our proposed approach, called Evolution Strategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, three variants of the star-minimax family of algorithms as well as a random controller in the Game of Carcassonne. We also use a variant of our proposed EA-based controller, dubbed ES partially integrated in MCTS. We show how the ES-MCTS controller, is able to outperform all these 10 intelligent controllers, including robust MCTS UCT controllers.
Real-Time Path-Guiding Based on Parametric Mixture Models
Venue: arXiv cs.GR 2021
Year: 2021

Path-Guiding algorithms for sampling scattering directions can drastically decrease the variance of Monte Carlo estimators of Light Transport Equation, but their usage was limited to offline rendering because of memory and computational limitations. We introduce a new robust screen-space technique that is based on online learning of parametric mixture models for guiding the real-time path-tracing algorithm. It requires storing of 8 parameters for every pixel, achieves a reduction of FLIP metric up to 4 times with 1 spp rendering. Also, it consumes less than 1.5ms on RTX 2070 for 1080p and reduces path-tracing timings by generating more coherent rays by about 5% on average. Moreover, it leads to significant bias reduction and a lower level of flickering of SVGF output.
Improving Ethical Outcomes with Machine-in-the-Loop: Broadening Human Understanding of Data Annotations
Venue: arXiv cs.CL 2021
Year: 2021

We introduce a machine-in-the-loop pipeline that aims to address root causes of unwanted bias in natural language based supervised machine learning tasks in the education domain. Learning from the experiences of students is foundational for education researchers, and academic administrators. 21st-century skills learned from experience are becoming a core part of college and career readiness as well as the hiring process in the new knowledge economy. Minoritized students demonstrate these skills in their daily lives, but documenting, assessing, and validating these skills is a huge problem for educational institutions. As an equity focused online platform, LivedX translates minoritized students' lived experiences into the 21st century skills, issues micro-credentials, and creates personal 21st century skills portfolio. To automate the micro credential mining from the natural language texts received from the students' submitted essays, we employed a bag-of-word model to construct a multi-output classifier. Despite our goal, our model initially exacerbated disparate impact on minoritized students. We used a machine-in-the-loop model development pipeline to address the problem and refine the aforementioned model to ensure fairness in its prediction.
Set Twister for Single-hop Node Classification
Venue: arXiv cs.LG 2021
Year: 2021

Node classification is a central task in relational learning, with the current state-of-the-art hinging on two key principles: (i) predictions are permutation-invariant to the ordering of a node's neighbors, and (ii) predictions are a function of the node's $r$-hop neighborhood topology and attributes, $r \geq 2$. Both graph neural networks and collective inference methods (e.g., belief propagation) rely on information from up to $r$-hops away. In this work, we study if the use of more powerful permutation-invariant functions can sometimes avoid the need for classifiers to collect information beyond $1$-hop. Towards this, we introduce a new architecture, the Set Twister, which generalizes DeepSets (Zaheer et al., 2017), a simple and widely-used permutation-invariant representation. Set Twister theoretically increases expressiveness of DeepSets, allowing it to capture higher-order dependencies, while keeping its simplicity and low computational cost. Empirically, we see accuracy improvements of Set Twister over DeepSets as well as a variety of graph neural networks and collective inference schemes in several tasks, while showcasing its implementation simplicity and computational efficiency.
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices. Unfortunately, current deep networks remain not only too compute-heavy for inference and training on edge devices, but also too large for communicating updates over bandwidth-constrained networks. In this paper, we develop, implement, and experimentally validate a novel FL framework termed Federated Dynamic Sparse Training (FedDST) by which complex neural networks can be deployed and trained with substantially improved efficiency in both on-device computation and in-network communication. At the core of FedDST is a dynamic process that extracts and trains sparse sub-networks from the target full network. With this scheme, "two birds are killed with one stone:" instead of full models, each client performs efficient training of its own sparse networks, and only sparse networks are transmitted between devices and the cloud. Furthermore, our results reveal that the dynamic sparsity during FL training more flexibly accommodates local heterogeneity in FL agents than the fixed, shared sparse masks. Moreover, dynamic sparsity naturally introduces an "in-time self-ensembling effect" into the training dynamics and improves the FL performance even over dense training. In a realistic and challenging non i.i.d. FL setting, FedDST consistently outperforms competing algorithms in our experiments: for instance, at any fixed upload data cap on non-iid CIFAR-10, it gains an impressive accuracy advantage of 10% over FedAvgM when given the same upload data cap; the accuracy gap remains 3% even when FedAvgM is given 2x the upload data cap, further demonstrating efficacy of FedDST. Code is available at: https://github.com/bibikar/feddst.
Does Explainable Machine Learning Uncover the Black Box in Vision Applications?
Venue: arXiv cs.CV 2021
Year: 2021

Machine learning (ML) in general and deep learning (DL) in particular has become an extremely popular tool in several vision applications (like object detection, super resolution, segmentation, object tracking etc.). Almost in parallel, the issue of explainability in ML (i.e. the ability to explain/elaborate the way a trained ML model arrived at its decision) in vision has also received fairly significant attention from various quarters. However, we argue that the current philosophy behind explainable ML suffers from certain limitations, and the resulting explanations may not meaningfully uncover black box ML models. To elaborate our assertion, we first raise a few fundamental questions which have not been adequately discussed in the corresponding literature. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles in the related areas.
A First Approach to Risk-Limiting Audits for Single Transferable Vote Elections
Venue: arXiv cs.CY 2021
Year: 2021

Risk-limiting audits (RLAs) are an increasingly important method for checking that the reported outcome of an election is, in fact, correct. Indeed, their use is increasingly being legislated. While effective methods for RLAs have been developed for many forms of election -- for example: first-past-the-post, instant-runoff voting, and D'Hondt elections -- auditing methods for single transferable vote (STV) elections have yet to be developed. STV elections are notoriously hard to reason about since there is a complex interaction of votes that change their value throughout the process. In this paper we present the first approach to risk-limiting audits for STV elections, restricted to the case of 2-seat STV elections.
Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets
Venue: arXiv cs.CL 2021
Year: 2021

In the current era of the internet, where social media platforms are easily accessible for everyone, people often have to deal with threats, identity attacks, hate, and bullying due to their association with a cast, creed, gender, religion, or even acceptance or rejection of a notion. Existing works in hate speech detection primarily focus on individual comment classification as a sequence labeling task and often fail to consider the context of the conversation. The context of a conversation often plays a substantial role when determining the author's intent and sentiment behind the tweet. This paper describes the system proposed by team MIDAS-IIITD for HASOC 2021 subtask 2, one of the first shared tasks focusing on detecting hate speech from Hindi-English code-mixed conversations on Twitter. We approach this problem using neural networks, leveraging the transformer's cross-lingual embeddings and further finetuning them for low-resource hate-speech classification in transliterated Hindi text. Our best performing system, a hard voting ensemble of Indic-BERT, XLM-RoBERTa, and Multilingual BERT, achieved a macro F1 score of 0.7253, placing us first on the overall leaderboard standings.
COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs
Venue: arXiv cs.DC 2021
Year: 2021

As CUDA programs become the de facto program among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms has been a compelling option. Although several efforts have attempted to support CUDA on other than NVIDIA GPU devices, due to extra steps in the translation, the support is always behind a few years from supporting CUDA's latest features. The examples are DPC, Hipfy, where CUDA source code have to be translated to their native supporting language and then they are supported. In particular, the new CUDA programming model exposes the warp concept in the programming language, which greatly changes the way the CUDA code should be mapped to CPU programs. In this paper, hierarchical collapsing that \emph{correctly} supports CUDA warp-level functions on CPUs is proposed. Based on hierarchical collapsing, a framework, COX, is developed that allows CUDA programs with the latest features to be executed efficiently on CPU platforms. COX consists of a compiler IR transformation (new LLVM pass) and a runtime system to execute the transformed programs on CPU devices. COX can support the most recent CUDA features, and the application coverage is much higher (90%) than for previous frameworks (68%) with comparable performance. We also show that the warp-level functions in CUDA can be efficiently executed by utilizing CPU SIMD (AVX) instructions.
CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding
Venue: arXiv cs.LG 2021
Year: 2021

Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.
ArcFace Knows the Gender, Too!
Venue: arXiv cs.CV 2021
Year: 2021

The main idea of this paper is that if a model can recognize a person, of course, it must be able to know the gender of that person, too. Therefore, instead of defining a new model for gender classification, this paper uses ArcFace features to determine gender, based on the facial features. A face image is given to ArcFace and 512 features are obtained for the face. Then, with the help of traditional machine learning models, gender is determined. Discriminative methods such as Support Vector Machine (SVM), Linear Discriminant, and Logistic Regression well demonstrate that the features extracted from the ArcFace create a remarkable distinction between the gender classes. Experiments on the Gender Classification Dataset show that SVM with Gaussian kernel is able to classify gender with an accuracy of 96.4% using ArcFace features.
New Mechanism for Fast System Calls
Venue: arXiv cs.OS 2021
Year: 2021

System calls have no place on the fast path of microsecond-scale systems. However, kernel bypass prevents the OS from controlling and supervising access to the hardware. In this paper we introduce the fastcall space, a new layer in the traditional OS architecture, that hosts fastcalls. A fastcall implements the fast path of a traditional kernel operation and can stay on the fast path, because the transition to the fastcall space is $\approx\times 15$ faster than to the kernel space. This way the OS does not give up the control over device access, whereas the applications maintain their performance.
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Venue: arXiv cs.RO 2021
Year: 2021

Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly simulation environment for furniture assembly. We formulate the part assembly task as a concrete reinforcement learning problem and propose a pipeline for robots to learn to assemble a diverse set of chairs. Experiments show that when testing with unseen chairs, our approach achieves a success rate of 74.5% under the object-centric setting and 50.0% under the full setting. We adopt an RRT-Connect algorithm as the baseline, which only achieves a success rate of 18.8% after a significantly longer computation time. Supplemental materials and videos are available on our project webpage.